{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sherwin/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/Users/sherwin/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 216, got 192\n",
      "  return f(*args, **kwds)\n",
      "/Users/sherwin/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/Users/sherwin/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 216, got 192\n",
      "  return f(*args, **kwds)\n",
      "/Users/sherwin/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/Users/sherwin/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 216, got 192\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>小区名称</th>\n",
       "      <th>户型</th>\n",
       "      <th>面积(㎡)</th>\n",
       "      <th>价格(元/月)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>万国城MOMA</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>59.11平米</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>东城</td>\n",
       "      <td>北官厅胡同2号院</td>\n",
       "      <td>3室0厅</td>\n",
       "      <td>56.92平米</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>东城</td>\n",
       "      <td>和平里三区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>40.57平米</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>东城</td>\n",
       "      <td>菊儿胡同</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>57.09平米</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>东城</td>\n",
       "      <td>交道口北二条35号院</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>42.67平米</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>东城</td>\n",
       "      <td>西营房</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>54.48平米</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>东城</td>\n",
       "      <td>地坛北门</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>33.76平米</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>东城</td>\n",
       "      <td>安外东河沿</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>37.62平米</td>\n",
       "      <td>5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>东城</td>\n",
       "      <td>清水苑</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>45.61平米</td>\n",
       "      <td>6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>东城</td>\n",
       "      <td>李村东里</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>57.35平米</td>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>东城</td>\n",
       "      <td>幸福北里</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>51.15平米</td>\n",
       "      <td>6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>东城</td>\n",
       "      <td>保利蔷薇</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>97.11平米</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>东城</td>\n",
       "      <td>东板桥西巷</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>52.86平米</td>\n",
       "      <td>5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>东城</td>\n",
       "      <td>本家润园三期</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>63.09平米</td>\n",
       "      <td>7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>东城</td>\n",
       "      <td>营房西街</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>62.95平米</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>东城</td>\n",
       "      <td>新景家园西区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>57.24平米</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>东城</td>\n",
       "      <td>东花市北里东区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>85.36平米</td>\n",
       "      <td>8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>东城</td>\n",
       "      <td>幸福家园一期</td>\n",
       "      <td>5室2厅</td>\n",
       "      <td>226.86平米</td>\n",
       "      <td>29000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>东城</td>\n",
       "      <td>景泰西里西区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>60.3平米</td>\n",
       "      <td>6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>东城</td>\n",
       "      <td>海晟名苑北区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>70.86平米</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>东城</td>\n",
       "      <td>和平新城一期</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>122.76平米</td>\n",
       "      <td>14500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>东城</td>\n",
       "      <td>太华公寓</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>152.24平米</td>\n",
       "      <td>17000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>东城</td>\n",
       "      <td>官书院</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>92.01平米</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>东城</td>\n",
       "      <td>幸福家园二期</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>65.25平米</td>\n",
       "      <td>7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>东城</td>\n",
       "      <td>安外大街3号院</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>33.77平米</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>东城</td>\n",
       "      <td>中海紫御公馆</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>90.15平米</td>\n",
       "      <td>13000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>东城</td>\n",
       "      <td>海晟名苑北区</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>45.62平米</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>东城</td>\n",
       "      <td>凯景铭座</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>156.2平米</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>东城</td>\n",
       "      <td>永定门东街西里</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>53.26平米</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>东城</td>\n",
       "      <td>西青年沟</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>51.88平米</td>\n",
       "      <td>7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8193</th>\n",
       "      <td>顺义</td>\n",
       "      <td>南竺园</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>90.47平米</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8194</th>\n",
       "      <td>顺义</td>\n",
       "      <td>石园东苑</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>102.94平米</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8195</th>\n",
       "      <td>顺义</td>\n",
       "      <td>建新北区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>52.44平米</td>\n",
       "      <td>3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8196</th>\n",
       "      <td>顺义</td>\n",
       "      <td>仓上小区</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>108.03平米</td>\n",
       "      <td>3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8197</th>\n",
       "      <td>顺义</td>\n",
       "      <td>石园东区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>91.93平米</td>\n",
       "      <td>4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8198</th>\n",
       "      <td>顺义</td>\n",
       "      <td>裕龙三区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>69.04平米</td>\n",
       "      <td>3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8199</th>\n",
       "      <td>顺义</td>\n",
       "      <td>建新北区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>50.04平米</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8200</th>\n",
       "      <td>顺义</td>\n",
       "      <td>东兴二区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>81.98平米</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8201</th>\n",
       "      <td>顺义</td>\n",
       "      <td>万科四季花城</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>98.71平米</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8202</th>\n",
       "      <td>顺义</td>\n",
       "      <td>石园南区18号院</td>\n",
       "      <td>1房间1卫</td>\n",
       "      <td>58.7平米</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8203</th>\n",
       "      <td>顺义</td>\n",
       "      <td>建新北区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>49.06平米</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8204</th>\n",
       "      <td>顺义</td>\n",
       "      <td>香悦四季</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>87.92平米</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8205</th>\n",
       "      <td>顺义</td>\n",
       "      <td>香悦四季</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>117.5平米</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8206</th>\n",
       "      <td>顺义</td>\n",
       "      <td>旭辉26街区</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>59平米</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8207</th>\n",
       "      <td>顺义</td>\n",
       "      <td>江山赋</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>97.38平米</td>\n",
       "      <td>8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8208</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园六区</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>94.78平米</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8209</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园二区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>68.76平米</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8210</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园六区</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>94.78平米</td>\n",
       "      <td>3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8211</th>\n",
       "      <td>顺义</td>\n",
       "      <td>胜利小区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>58.05平米</td>\n",
       "      <td>3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8212</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园一区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>79.59平米</td>\n",
       "      <td>3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8213</th>\n",
       "      <td>顺义</td>\n",
       "      <td>江山赋</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>74.62平米</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8214</th>\n",
       "      <td>顺义</td>\n",
       "      <td>江山赋</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>104.03平米</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8215</th>\n",
       "      <td>顺义</td>\n",
       "      <td>恒华安纳湖</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>90.43平米</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8216</th>\n",
       "      <td>顺义</td>\n",
       "      <td>石园北区</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>90.67平米</td>\n",
       "      <td>3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8217</th>\n",
       "      <td>顺义</td>\n",
       "      <td>江山赋</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>146.92平米</td>\n",
       "      <td>17000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8218</th>\n",
       "      <td>顺义</td>\n",
       "      <td>怡馨家园</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>114.03平米</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8219</th>\n",
       "      <td>顺义</td>\n",
       "      <td>旭辉26街区</td>\n",
       "      <td>4房间2卫</td>\n",
       "      <td>59平米</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8220</th>\n",
       "      <td>顺义</td>\n",
       "      <td>前进花园玉兰苑</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>92.41平米</td>\n",
       "      <td>5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8221</th>\n",
       "      <td>顺义</td>\n",
       "      <td>双裕小区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>71.81平米</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8222</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园二区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>35.43平米</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8223 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      区域        小区名称     户型     面积(㎡)  价格(元/月)\n",
       "0     东城     万国城MOMA   1室0厅   59.11平米    10000\n",
       "1     东城    北官厅胡同2号院   3室0厅   56.92平米     6000\n",
       "2     东城       和平里三区   1室1厅   40.57平米     6900\n",
       "3     东城        菊儿胡同   2室1厅   57.09平米     8000\n",
       "4     东城  交道口北二条35号院   1室1厅   42.67平米     5500\n",
       "5     东城         西营房   2室1厅   54.48平米     7200\n",
       "6     东城        地坛北门   1室1厅   33.76平米     6000\n",
       "7     东城       安外东河沿   1室1厅   37.62平米     5600\n",
       "8     东城         清水苑   1室1厅   45.61平米     6200\n",
       "9     东城        李村东里   2室1厅   57.35平米     5700\n",
       "10    东城        幸福北里   2室1厅   51.15平米     6500\n",
       "11    东城        保利蔷薇   2室1厅   97.11平米    10000\n",
       "12    东城       东板桥西巷   2室1厅   52.86平米     5800\n",
       "13    东城      本家润园三期   2室1厅   63.09平米     7800\n",
       "14    东城        营房西街   2室1厅   62.95平米     7500\n",
       "15    东城      新景家园西区   1室1厅   57.24平米     7500\n",
       "16    东城     东花市北里东区   2室1厅   85.36平米     8800\n",
       "17    东城      幸福家园一期   5室2厅  226.86平米    29000\n",
       "18    东城      景泰西里西区   1室1厅    60.3平米     6200\n",
       "19    东城      海晟名苑北区   1室1厅   70.86平米    12000\n",
       "20    东城      和平新城一期   2室1厅  122.76平米    14500\n",
       "21    东城        太华公寓   2室2厅  152.24平米    17000\n",
       "22    东城         官书院   2室1厅   92.01平米    16000\n",
       "23    东城      幸福家园二期   2室1厅   65.25平米     7800\n",
       "24    东城     安外大街3号院   1室1厅   33.77平米     5500\n",
       "25    东城      中海紫御公馆   2室2厅   90.15平米    13000\n",
       "26    东城      海晟名苑北区   1室0厅   45.62平米     9000\n",
       "27    东城        凯景铭座   3室1厅   156.2平米    16000\n",
       "28    东城     永定门东街西里   2室1厅   53.26平米     5000\n",
       "29    东城        西青年沟   2室1厅   51.88平米     7300\n",
       "...   ..         ...    ...       ...      ...\n",
       "8193  顺义         南竺园   3室1厅   90.47平米     4200\n",
       "8194  顺义        石园东苑   2室2厅  102.94平米     4000\n",
       "8195  顺义        建新北区   2室1厅   52.44平米     3200\n",
       "8196  顺义        仓上小区   3室1厅  108.03平米     3900\n",
       "8197  顺义        石园东区   2室1厅   91.93平米     4100\n",
       "8198  顺义        裕龙三区   1室1厅   69.04平米     3800\n",
       "8199  顺义        建新北区   2室1厅   50.04平米     3600\n",
       "8200  顺义        东兴二区   2室1厅   81.98平米     4000\n",
       "8201  顺义      万科四季花城   2室1厅   98.71平米     6000\n",
       "8202  顺义    石园南区18号院  1房间1卫    58.7平米     4000\n",
       "8203  顺义        建新北区   2室1厅   49.06平米     3600\n",
       "8204  顺义        香悦四季   3室1厅   87.92平米     4200\n",
       "8205  顺义        香悦四季   3室2厅   117.5平米     6000\n",
       "8206  顺义      旭辉26街区   3室1厅      59平米     4500\n",
       "8207  顺义         江山赋   2室1厅   97.38平米     8800\n",
       "8208  顺义       樱花园六区   2室2厅   94.78平米     4200\n",
       "8209  顺义       樱花园二区   2室1厅   68.76平米     3500\n",
       "8210  顺义       樱花园六区   2室2厅   94.78平米     3900\n",
       "8211  顺义        胜利小区   2室1厅   58.05平米     3800\n",
       "8212  顺义       樱花园一区   2室1厅   79.59平米     3900\n",
       "8213  顺义         江山赋   2室1厅   74.62平米     5200\n",
       "8214  顺义         江山赋   3室1厅  104.03平米     9500\n",
       "8215  顺义       恒华安纳湖   2室1厅   90.43平米     3500\n",
       "8216  顺义        石园北区   2室2厅   90.67平米     3700\n",
       "8217  顺义         江山赋   3室2厅  146.92平米    17000\n",
       "8218  顺义        怡馨家园   3室1厅  114.03平米     5500\n",
       "8219  顺义      旭辉26街区  4房间2卫      59平米     5000\n",
       "8220  顺义     前进花园玉兰苑   3室1厅   92.41平米     5800\n",
       "8221  顺义        双裕小区   2室1厅   71.81平米     4200\n",
       "8222  顺义       樱花园二区   1室1厅   35.43平米     2700\n",
       "\n",
       "[8223 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data = pd.read_csv(\"./data/链家北京租房数据.csv\")\n",
    "file_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8223, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8223 entries, 0 to 8222\n",
      "Data columns (total 5 columns):\n",
      "区域         8223 non-null object\n",
      "小区名称       8223 non-null object\n",
      "户型         8223 non-null object\n",
      "面积(㎡)      8223 non-null object\n",
      "价格(元/月)    8223 non-null int64\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 321.3+ KB\n"
     ]
    }
   ],
   "source": [
    "file_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>价格(元/月)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8223.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9512.297823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9186.752612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>566.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             价格(元/月)\n",
       "count    8223.000000\n",
       "mean     9512.297823\n",
       "std      9186.752612\n",
       "min       566.000000\n",
       "25%      4800.000000\n",
       "50%      6800.000000\n",
       "75%     10000.000000\n",
       "max    150000.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据基本处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 重复值和空值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重复值\n",
    "# file_data.duplicated()\n",
    "\n",
    "file_data = file_data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5773, 5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 空值处理\n",
    "file_data = file_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5773, 5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据转换类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 面积数据类型转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>小区名称</th>\n",
       "      <th>户型</th>\n",
       "      <th>面积(㎡)</th>\n",
       "      <th>价格(元/月)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>万国城MOMA</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>59.11平米</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>东城</td>\n",
       "      <td>北官厅胡同2号院</td>\n",
       "      <td>3室0厅</td>\n",
       "      <td>56.92平米</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>东城</td>\n",
       "      <td>和平里三区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>40.57平米</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>东城</td>\n",
       "      <td>菊儿胡同</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>57.09平米</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>东城</td>\n",
       "      <td>交道口北二条35号院</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>42.67平米</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   区域        小区名称    户型    面积(㎡)  价格(元/月)\n",
       "0  东城     万国城MOMA  1室0厅  59.11平米    10000\n",
       "1  东城    北官厅胡同2号院  3室0厅  56.92平米     6000\n",
       "2  东城       和平里三区  1室1厅  40.57平米     6900\n",
       "3  东城        菊儿胡同  2室1厅  57.09平米     8000\n",
       "4  东城  交道口北二条35号院  1室1厅  42.67平米     5500"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'59.11'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 单个值实现\n",
    "file_data[\"面积(㎡)\"].values[0][:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建一个空的数组\n",
    "data_new = np.array([])\n",
    "\n",
    "data_area = file_data[\"面积(㎡)\"].values\n",
    "\n",
    "for i in data_area:\n",
    "    data_new = np.append(data_new, np.array(i[:-2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['59.11平米', '56.92平米', '40.57平米', ..., '92.41平米', '71.81平米',\n",
       "       '35.43平米'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['59.11', '56.92', '40.57', ..., '92.41', '71.81', '35.43'],\n",
       "      dtype='<U32')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 转换data_new中的数据类型\n",
    "data_new = data_new.astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([59.11, 56.92, 40.57, ..., 92.41, 71.81, 35.43])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_data.loc[:, \"面积(㎡)\"] = data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>小区名称</th>\n",
       "      <th>户型</th>\n",
       "      <th>面积(㎡)</th>\n",
       "      <th>价格(元/月)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>万国城MOMA</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>59.11</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>东城</td>\n",
       "      <td>北官厅胡同2号院</td>\n",
       "      <td>3室0厅</td>\n",
       "      <td>56.92</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>东城</td>\n",
       "      <td>和平里三区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>40.57</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>东城</td>\n",
       "      <td>菊儿胡同</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>57.09</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>东城</td>\n",
       "      <td>交道口北二条35号院</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>42.67</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   区域        小区名称    户型  面积(㎡)  价格(元/月)\n",
       "0  东城     万国城MOMA  1室0厅  59.11    10000\n",
       "1  东城    北官厅胡同2号院  3室0厅  56.92     6000\n",
       "2  东城       和平里三区  1室1厅  40.57     6900\n",
       "3  东城        菊儿胡同  2室1厅  57.09     8000\n",
       "4  东城  交道口北二条35号院  1室1厅  42.67     5500"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 户型表达方式替换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>小区名称</th>\n",
       "      <th>户型</th>\n",
       "      <th>面积(㎡)</th>\n",
       "      <th>价格(元/月)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>万国城MOMA</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>59.11</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>东城</td>\n",
       "      <td>北官厅胡同2号院</td>\n",
       "      <td>3室0厅</td>\n",
       "      <td>56.92</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>东城</td>\n",
       "      <td>和平里三区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>40.57</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>东城</td>\n",
       "      <td>菊儿胡同</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>57.09</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>东城</td>\n",
       "      <td>交道口北二条35号院</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>42.67</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>东城</td>\n",
       "      <td>西营房</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>54.48</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>东城</td>\n",
       "      <td>地坛北门</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>33.76</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>东城</td>\n",
       "      <td>安外东河沿</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>37.62</td>\n",
       "      <td>5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>东城</td>\n",
       "      <td>清水苑</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>45.61</td>\n",
       "      <td>6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>东城</td>\n",
       "      <td>李村东里</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>57.35</td>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>东城</td>\n",
       "      <td>幸福北里</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>51.15</td>\n",
       "      <td>6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>东城</td>\n",
       "      <td>保利蔷薇</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>97.11</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>东城</td>\n",
       "      <td>东板桥西巷</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>52.86</td>\n",
       "      <td>5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>东城</td>\n",
       "      <td>本家润园三期</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>63.09</td>\n",
       "      <td>7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>东城</td>\n",
       "      <td>营房西街</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>62.95</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>东城</td>\n",
       "      <td>新景家园西区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>57.24</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>东城</td>\n",
       "      <td>东花市北里东区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>85.36</td>\n",
       "      <td>8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>东城</td>\n",
       "      <td>幸福家园一期</td>\n",
       "      <td>5室2厅</td>\n",
       "      <td>226.86</td>\n",
       "      <td>29000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>东城</td>\n",
       "      <td>景泰西里西区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>60.30</td>\n",
       "      <td>6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>东城</td>\n",
       "      <td>海晟名苑北区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>70.86</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>东城</td>\n",
       "      <td>和平新城一期</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>122.76</td>\n",
       "      <td>14500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>东城</td>\n",
       "      <td>太华公寓</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>152.24</td>\n",
       "      <td>17000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>东城</td>\n",
       "      <td>官书院</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>92.01</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>东城</td>\n",
       "      <td>幸福家园二期</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>65.25</td>\n",
       "      <td>7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>东城</td>\n",
       "      <td>安外大街3号院</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>33.77</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>东城</td>\n",
       "      <td>中海紫御公馆</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>90.15</td>\n",
       "      <td>13000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>东城</td>\n",
       "      <td>海晟名苑北区</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>45.62</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>东城</td>\n",
       "      <td>凯景铭座</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>156.20</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>东城</td>\n",
       "      <td>永定门东街西里</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>53.26</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>东城</td>\n",
       "      <td>西青年沟</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>51.88</td>\n",
       "      <td>7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8173</th>\n",
       "      <td>顺义</td>\n",
       "      <td>智地香蜜湾</td>\n",
       "      <td>4房间2卫</td>\n",
       "      <td>75.87</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8174</th>\n",
       "      <td>顺义</td>\n",
       "      <td>香花畦家园</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>179.65</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8175</th>\n",
       "      <td>顺义</td>\n",
       "      <td>龙湖香醍漫步四区南区</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>38.96</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8177</th>\n",
       "      <td>顺义</td>\n",
       "      <td>莫奈花园</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>241.78</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8181</th>\n",
       "      <td>顺义</td>\n",
       "      <td>裕龙三区</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>152.72</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8183</th>\n",
       "      <td>顺义</td>\n",
       "      <td>万科城市花园</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>218.44</td>\n",
       "      <td>15500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8192</th>\n",
       "      <td>顺义</td>\n",
       "      <td>胜利小区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>43.80</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8193</th>\n",
       "      <td>顺义</td>\n",
       "      <td>南竺园</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>90.47</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8200</th>\n",
       "      <td>顺义</td>\n",
       "      <td>东兴二区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>81.98</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8201</th>\n",
       "      <td>顺义</td>\n",
       "      <td>万科四季花城</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>98.71</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8202</th>\n",
       "      <td>顺义</td>\n",
       "      <td>石园南区18号院</td>\n",
       "      <td>1房间1卫</td>\n",
       "      <td>58.70</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8203</th>\n",
       "      <td>顺义</td>\n",
       "      <td>建新北区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>49.06</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8204</th>\n",
       "      <td>顺义</td>\n",
       "      <td>香悦四季</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>87.92</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8205</th>\n",
       "      <td>顺义</td>\n",
       "      <td>香悦四季</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>117.50</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8206</th>\n",
       "      <td>顺义</td>\n",
       "      <td>旭辉26街区</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>59.00</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8207</th>\n",
       "      <td>顺义</td>\n",
       "      <td>江山赋</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>97.38</td>\n",
       "      <td>8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8208</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园六区</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>94.78</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8209</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园二区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>68.76</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8210</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园六区</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>94.78</td>\n",
       "      <td>3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8211</th>\n",
       "      <td>顺义</td>\n",
       "      <td>胜利小区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>58.05</td>\n",
       "      <td>3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8212</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园一区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>79.59</td>\n",
       "      <td>3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8214</th>\n",
       "      <td>顺义</td>\n",
       "      <td>江山赋</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>104.03</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8215</th>\n",
       "      <td>顺义</td>\n",
       "      <td>恒华安纳湖</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>90.43</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8216</th>\n",
       "      <td>顺义</td>\n",
       "      <td>石园北区</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>90.67</td>\n",
       "      <td>3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8217</th>\n",
       "      <td>顺义</td>\n",
       "      <td>江山赋</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>146.92</td>\n",
       "      <td>17000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8218</th>\n",
       "      <td>顺义</td>\n",
       "      <td>怡馨家园</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>114.03</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8219</th>\n",
       "      <td>顺义</td>\n",
       "      <td>旭辉26街区</td>\n",
       "      <td>4房间2卫</td>\n",
       "      <td>59.00</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8220</th>\n",
       "      <td>顺义</td>\n",
       "      <td>前进花园玉兰苑</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>92.41</td>\n",
       "      <td>5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8221</th>\n",
       "      <td>顺义</td>\n",
       "      <td>双裕小区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>71.81</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8222</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园二区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>35.43</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5773 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      区域        小区名称     户型   面积(㎡)  价格(元/月)\n",
       "0     东城     万国城MOMA   1室0厅   59.11    10000\n",
       "1     东城    北官厅胡同2号院   3室0厅   56.92     6000\n",
       "2     东城       和平里三区   1室1厅   40.57     6900\n",
       "3     东城        菊儿胡同   2室1厅   57.09     8000\n",
       "4     东城  交道口北二条35号院   1室1厅   42.67     5500\n",
       "5     东城         西营房   2室1厅   54.48     7200\n",
       "6     东城        地坛北门   1室1厅   33.76     6000\n",
       "7     东城       安外东河沿   1室1厅   37.62     5600\n",
       "8     东城         清水苑   1室1厅   45.61     6200\n",
       "9     东城        李村东里   2室1厅   57.35     5700\n",
       "10    东城        幸福北里   2室1厅   51.15     6500\n",
       "11    东城        保利蔷薇   2室1厅   97.11    10000\n",
       "12    东城       东板桥西巷   2室1厅   52.86     5800\n",
       "13    东城      本家润园三期   2室1厅   63.09     7800\n",
       "14    东城        营房西街   2室1厅   62.95     7500\n",
       "15    东城      新景家园西区   1室1厅   57.24     7500\n",
       "16    东城     东花市北里东区   2室1厅   85.36     8800\n",
       "17    东城      幸福家园一期   5室2厅  226.86    29000\n",
       "18    东城      景泰西里西区   1室1厅   60.30     6200\n",
       "19    东城      海晟名苑北区   1室1厅   70.86    12000\n",
       "20    东城      和平新城一期   2室1厅  122.76    14500\n",
       "21    东城        太华公寓   2室2厅  152.24    17000\n",
       "22    东城         官书院   2室1厅   92.01    16000\n",
       "23    东城      幸福家园二期   2室1厅   65.25     7800\n",
       "24    东城     安外大街3号院   1室1厅   33.77     5500\n",
       "25    东城      中海紫御公馆   2室2厅   90.15    13000\n",
       "26    东城      海晟名苑北区   1室0厅   45.62     9000\n",
       "27    东城        凯景铭座   3室1厅  156.20    16000\n",
       "28    东城     永定门东街西里   2室1厅   53.26     5000\n",
       "29    东城        西青年沟   2室1厅   51.88     7300\n",
       "...   ..         ...    ...     ...      ...\n",
       "8173  顺义       智地香蜜湾  4房间2卫   75.87     8000\n",
       "8174  顺义       香花畦家园   4室2厅  179.65    12000\n",
       "8175  顺义  龙湖香醍漫步四区南区   1室0厅   38.96     2300\n",
       "8177  顺义        莫奈花园   4室2厅  241.78    20000\n",
       "8181  顺义        裕龙三区   3室1厅  152.72     9000\n",
       "8183  顺义      万科城市花园   4室2厅  218.44    15500\n",
       "8192  顺义        胜利小区   1室1厅   43.80     3000\n",
       "8193  顺义         南竺园   3室1厅   90.47     4200\n",
       "8200  顺义        东兴二区   2室1厅   81.98     4000\n",
       "8201  顺义      万科四季花城   2室1厅   98.71     6000\n",
       "8202  顺义    石园南区18号院  1房间1卫   58.70     4000\n",
       "8203  顺义        建新北区   2室1厅   49.06     3600\n",
       "8204  顺义        香悦四季   3室1厅   87.92     4200\n",
       "8205  顺义        香悦四季   3室2厅  117.50     6000\n",
       "8206  顺义      旭辉26街区   3室1厅   59.00     4500\n",
       "8207  顺义         江山赋   2室1厅   97.38     8800\n",
       "8208  顺义       樱花园六区   2室2厅   94.78     4200\n",
       "8209  顺义       樱花园二区   2室1厅   68.76     3500\n",
       "8210  顺义       樱花园六区   2室2厅   94.78     3900\n",
       "8211  顺义        胜利小区   2室1厅   58.05     3800\n",
       "8212  顺义       樱花园一区   2室1厅   79.59     3900\n",
       "8214  顺义         江山赋   3室1厅  104.03     9500\n",
       "8215  顺义       恒华安纳湖   2室1厅   90.43     3500\n",
       "8216  顺义        石园北区   2室2厅   90.67     3700\n",
       "8217  顺义         江山赋   3室2厅  146.92    17000\n",
       "8218  顺义        怡馨家园   3室1厅  114.03     5500\n",
       "8219  顺义      旭辉26街区  4房间2卫   59.00     5000\n",
       "8220  顺义     前进花园玉兰苑   3室1厅   92.41     5800\n",
       "8221  顺义        双裕小区   2室1厅   71.81     4200\n",
       "8222  顺义       樱花园二区   1室1厅   35.43     2700\n",
       "\n",
       "[5773 rows x 5 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_data = file_data[\"户型\"]\n",
    "temp_list = []\n",
    "\n",
    "for i in house_data:\n",
    "    # print(i)\n",
    "    new_info = i.replace(\"房间\", \"室\")\n",
    "    temp_list.append(new_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_data.loc[:, \"户型\"] = temp_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>小区名称</th>\n",
       "      <th>户型</th>\n",
       "      <th>面积(㎡)</th>\n",
       "      <th>价格(元/月)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>万国城MOMA</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>59.11</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>东城</td>\n",
       "      <td>北官厅胡同2号院</td>\n",
       "      <td>3室0厅</td>\n",
       "      <td>56.92</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>东城</td>\n",
       "      <td>和平里三区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>40.57</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>东城</td>\n",
       "      <td>菊儿胡同</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>57.09</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>东城</td>\n",
       "      <td>交道口北二条35号院</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>42.67</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>东城</td>\n",
       "      <td>西营房</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>54.48</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>东城</td>\n",
       "      <td>地坛北门</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>33.76</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>东城</td>\n",
       "      <td>安外东河沿</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>37.62</td>\n",
       "      <td>5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>东城</td>\n",
       "      <td>清水苑</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>45.61</td>\n",
       "      <td>6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>东城</td>\n",
       "      <td>李村东里</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>57.35</td>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>东城</td>\n",
       "      <td>幸福北里</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>51.15</td>\n",
       "      <td>6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>东城</td>\n",
       "      <td>保利蔷薇</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>97.11</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>东城</td>\n",
       "      <td>东板桥西巷</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>52.86</td>\n",
       "      <td>5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>东城</td>\n",
       "      <td>本家润园三期</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>63.09</td>\n",
       "      <td>7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>东城</td>\n",
       "      <td>营房西街</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>62.95</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>东城</td>\n",
       "      <td>新景家园西区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>57.24</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>东城</td>\n",
       "      <td>东花市北里东区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>85.36</td>\n",
       "      <td>8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>东城</td>\n",
       "      <td>幸福家园一期</td>\n",
       "      <td>5室2厅</td>\n",
       "      <td>226.86</td>\n",
       "      <td>29000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>东城</td>\n",
       "      <td>景泰西里西区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>60.30</td>\n",
       "      <td>6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>东城</td>\n",
       "      <td>海晟名苑北区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>70.86</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>东城</td>\n",
       "      <td>和平新城一期</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>122.76</td>\n",
       "      <td>14500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>东城</td>\n",
       "      <td>太华公寓</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>152.24</td>\n",
       "      <td>17000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>东城</td>\n",
       "      <td>官书院</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>92.01</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>东城</td>\n",
       "      <td>幸福家园二期</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>65.25</td>\n",
       "      <td>7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>东城</td>\n",
       "      <td>安外大街3号院</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>33.77</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>东城</td>\n",
       "      <td>中海紫御公馆</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>90.15</td>\n",
       "      <td>13000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>东城</td>\n",
       "      <td>海晟名苑北区</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>45.62</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>东城</td>\n",
       "      <td>凯景铭座</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>156.20</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>东城</td>\n",
       "      <td>永定门东街西里</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>53.26</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>东城</td>\n",
       "      <td>西青年沟</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>51.88</td>\n",
       "      <td>7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8173</th>\n",
       "      <td>顺义</td>\n",
       "      <td>智地香蜜湾</td>\n",
       "      <td>4室2卫</td>\n",
       "      <td>75.87</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8174</th>\n",
       "      <td>顺义</td>\n",
       "      <td>香花畦家园</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>179.65</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8175</th>\n",
       "      <td>顺义</td>\n",
       "      <td>龙湖香醍漫步四区南区</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>38.96</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8177</th>\n",
       "      <td>顺义</td>\n",
       "      <td>莫奈花园</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>241.78</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8181</th>\n",
       "      <td>顺义</td>\n",
       "      <td>裕龙三区</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>152.72</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8183</th>\n",
       "      <td>顺义</td>\n",
       "      <td>万科城市花园</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>218.44</td>\n",
       "      <td>15500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8192</th>\n",
       "      <td>顺义</td>\n",
       "      <td>胜利小区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>43.80</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8193</th>\n",
       "      <td>顺义</td>\n",
       "      <td>南竺园</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>90.47</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8200</th>\n",
       "      <td>顺义</td>\n",
       "      <td>东兴二区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>81.98</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8201</th>\n",
       "      <td>顺义</td>\n",
       "      <td>万科四季花城</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>98.71</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8202</th>\n",
       "      <td>顺义</td>\n",
       "      <td>石园南区18号院</td>\n",
       "      <td>1室1卫</td>\n",
       "      <td>58.70</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8203</th>\n",
       "      <td>顺义</td>\n",
       "      <td>建新北区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>49.06</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8204</th>\n",
       "      <td>顺义</td>\n",
       "      <td>香悦四季</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>87.92</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8205</th>\n",
       "      <td>顺义</td>\n",
       "      <td>香悦四季</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>117.50</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8206</th>\n",
       "      <td>顺义</td>\n",
       "      <td>旭辉26街区</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>59.00</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8207</th>\n",
       "      <td>顺义</td>\n",
       "      <td>江山赋</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>97.38</td>\n",
       "      <td>8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8208</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园六区</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>94.78</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8209</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园二区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>68.76</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8210</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园六区</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>94.78</td>\n",
       "      <td>3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8211</th>\n",
       "      <td>顺义</td>\n",
       "      <td>胜利小区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>58.05</td>\n",
       "      <td>3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8212</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园一区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>79.59</td>\n",
       "      <td>3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8214</th>\n",
       "      <td>顺义</td>\n",
       "      <td>江山赋</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>104.03</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8215</th>\n",
       "      <td>顺义</td>\n",
       "      <td>恒华安纳湖</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>90.43</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8216</th>\n",
       "      <td>顺义</td>\n",
       "      <td>石园北区</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>90.67</td>\n",
       "      <td>3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8217</th>\n",
       "      <td>顺义</td>\n",
       "      <td>江山赋</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>146.92</td>\n",
       "      <td>17000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8218</th>\n",
       "      <td>顺义</td>\n",
       "      <td>怡馨家园</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>114.03</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8219</th>\n",
       "      <td>顺义</td>\n",
       "      <td>旭辉26街区</td>\n",
       "      <td>4室2卫</td>\n",
       "      <td>59.00</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8220</th>\n",
       "      <td>顺义</td>\n",
       "      <td>前进花园玉兰苑</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>92.41</td>\n",
       "      <td>5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8221</th>\n",
       "      <td>顺义</td>\n",
       "      <td>双裕小区</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>71.81</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8222</th>\n",
       "      <td>顺义</td>\n",
       "      <td>樱花园二区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>35.43</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5773 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      区域        小区名称    户型   面积(㎡)  价格(元/月)\n",
       "0     东城     万国城MOMA  1室0厅   59.11    10000\n",
       "1     东城    北官厅胡同2号院  3室0厅   56.92     6000\n",
       "2     东城       和平里三区  1室1厅   40.57     6900\n",
       "3     东城        菊儿胡同  2室1厅   57.09     8000\n",
       "4     东城  交道口北二条35号院  1室1厅   42.67     5500\n",
       "5     东城         西营房  2室1厅   54.48     7200\n",
       "6     东城        地坛北门  1室1厅   33.76     6000\n",
       "7     东城       安外东河沿  1室1厅   37.62     5600\n",
       "8     东城         清水苑  1室1厅   45.61     6200\n",
       "9     东城        李村东里  2室1厅   57.35     5700\n",
       "10    东城        幸福北里  2室1厅   51.15     6500\n",
       "11    东城        保利蔷薇  2室1厅   97.11    10000\n",
       "12    东城       东板桥西巷  2室1厅   52.86     5800\n",
       "13    东城      本家润园三期  2室1厅   63.09     7800\n",
       "14    东城        营房西街  2室1厅   62.95     7500\n",
       "15    东城      新景家园西区  1室1厅   57.24     7500\n",
       "16    东城     东花市北里东区  2室1厅   85.36     8800\n",
       "17    东城      幸福家园一期  5室2厅  226.86    29000\n",
       "18    东城      景泰西里西区  1室1厅   60.30     6200\n",
       "19    东城      海晟名苑北区  1室1厅   70.86    12000\n",
       "20    东城      和平新城一期  2室1厅  122.76    14500\n",
       "21    东城        太华公寓  2室2厅  152.24    17000\n",
       "22    东城         官书院  2室1厅   92.01    16000\n",
       "23    东城      幸福家园二期  2室1厅   65.25     7800\n",
       "24    东城     安外大街3号院  1室1厅   33.77     5500\n",
       "25    东城      中海紫御公馆  2室2厅   90.15    13000\n",
       "26    东城      海晟名苑北区  1室0厅   45.62     9000\n",
       "27    东城        凯景铭座  3室1厅  156.20    16000\n",
       "28    东城     永定门东街西里  2室1厅   53.26     5000\n",
       "29    东城        西青年沟  2室1厅   51.88     7300\n",
       "...   ..         ...   ...     ...      ...\n",
       "8173  顺义       智地香蜜湾  4室2卫   75.87     8000\n",
       "8174  顺义       香花畦家园  4室2厅  179.65    12000\n",
       "8175  顺义  龙湖香醍漫步四区南区  1室0厅   38.96     2300\n",
       "8177  顺义        莫奈花园  4室2厅  241.78    20000\n",
       "8181  顺义        裕龙三区  3室1厅  152.72     9000\n",
       "8183  顺义      万科城市花园  4室2厅  218.44    15500\n",
       "8192  顺义        胜利小区  1室1厅   43.80     3000\n",
       "8193  顺义         南竺园  3室1厅   90.47     4200\n",
       "8200  顺义        东兴二区  2室1厅   81.98     4000\n",
       "8201  顺义      万科四季花城  2室1厅   98.71     6000\n",
       "8202  顺义    石园南区18号院  1室1卫   58.70     4000\n",
       "8203  顺义        建新北区  2室1厅   49.06     3600\n",
       "8204  顺义        香悦四季  3室1厅   87.92     4200\n",
       "8205  顺义        香悦四季  3室2厅  117.50     6000\n",
       "8206  顺义      旭辉26街区  3室1厅   59.00     4500\n",
       "8207  顺义         江山赋  2室1厅   97.38     8800\n",
       "8208  顺义       樱花园六区  2室2厅   94.78     4200\n",
       "8209  顺义       樱花园二区  2室1厅   68.76     3500\n",
       "8210  顺义       樱花园六区  2室2厅   94.78     3900\n",
       "8211  顺义        胜利小区  2室1厅   58.05     3800\n",
       "8212  顺义       樱花园一区  2室1厅   79.59     3900\n",
       "8214  顺义         江山赋  3室1厅  104.03     9500\n",
       "8215  顺义       恒华安纳湖  2室1厅   90.43     3500\n",
       "8216  顺义        石园北区  2室2厅   90.67     3700\n",
       "8217  顺义         江山赋  3室2厅  146.92    17000\n",
       "8218  顺义        怡馨家园  3室1厅  114.03     5500\n",
       "8219  顺义      旭辉26街区  4室2卫   59.00     5000\n",
       "8220  顺义     前进花园玉兰苑  3室1厅   92.41     5800\n",
       "8221  顺义        双裕小区  2室1厅   71.81     4200\n",
       "8222  顺义       樱花园二区  1室1厅   35.43     2700\n",
       "\n",
       "[5773 rows x 5 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 图表分析\n",
    "\n",
    "## 房源数量、位置分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['东城', '丰台', '亦庄开发区', '大兴', '房山', '昌平', '朝阳', '海淀', '石景山', '西城',\n",
       "       '通州', '门头沟', '顺义'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data[\"区域\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.DataFrame({\"区域\":file_data[\"区域\"].unique(), \"数量\":[0]*13})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>房山</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>昌平</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>海淀</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>石景山</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>西城</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>通州</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>顺义</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       区域  数量\n",
       "0      东城   0\n",
       "1      丰台   0\n",
       "2   亦庄开发区   0\n",
       "3      大兴   0\n",
       "4      房山   0\n",
       "5      昌平   0\n",
       "6      朝阳   0\n",
       "7      海淀   0\n",
       "8     石景山   0\n",
       "9      西城   0\n",
       "10     通州   0\n",
       "11    门头沟   0\n",
       "12     顺义   0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取每个区域房源数量\n",
    "area_count = file_data.groupby(by=\"区域\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df[\"数量\"] = area_count.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>1597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>海淀</td>\n",
       "      <td>605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>通州</td>\n",
       "      <td>477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>西城</td>\n",
       "      <td>442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>昌平</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>顺义</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>房山</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>石景山</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       区域    数量\n",
       "6      朝阳  1597\n",
       "7      海淀   605\n",
       "1      丰台   577\n",
       "10     通州   477\n",
       "9      西城   442\n",
       "3      大兴   362\n",
       "5      昌平   347\n",
       "12     顺义   297\n",
       "11    门头沟   285\n",
       "0      东城   282\n",
       "4      房山   180\n",
       "8     石景山   175\n",
       "2   亦庄开发区   147"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.sort_values(by=\"数量\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 户型数量分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1室0厅\n",
       "1    3室0厅\n",
       "2    1室1厅\n",
       "3    2室1厅\n",
       "4    1室1厅\n",
       "Name: 户型, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_data = file_data[\"户型\"]\n",
    "house_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def all_house(arr):\n",
    "    key = np.unique(arr)\n",
    "    result = {}\n",
    "    \n",
    "    for k in key:\n",
    "        mask = (arr == k)\n",
    "        arr_new = arr[mask]\n",
    "        v = arr_new.size\n",
    "        result[k] = v\n",
    "        \n",
    "    return result    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_info = all_house(house_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0室0厅': 1,\n",
       " '1室0卫': 10,\n",
       " '1室0厅': 244,\n",
       " '1室1卫': 126,\n",
       " '1室1厅': 844,\n",
       " '1室2厅': 13,\n",
       " '2室0卫': 1,\n",
       " '2室0厅': 23,\n",
       " '2室1卫': 120,\n",
       " '2室1厅': 2249,\n",
       " '2室2卫': 22,\n",
       " '2室2厅': 265,\n",
       " '2室3厅': 1,\n",
       " '3室0卫': 3,\n",
       " '3室0厅': 12,\n",
       " '3室1卫': 92,\n",
       " '3室1厅': 766,\n",
       " '3室2卫': 48,\n",
       " '3室2厅': 489,\n",
       " '3室3卫': 1,\n",
       " '3室3厅': 10,\n",
       " '4室1卫': 15,\n",
       " '4室1厅': 58,\n",
       " '4室2卫': 24,\n",
       " '4室2厅': 191,\n",
       " '4室3卫': 5,\n",
       " '4室3厅': 9,\n",
       " '4室5厅': 2,\n",
       " '5室0卫': 1,\n",
       " '5室0厅': 1,\n",
       " '5室1卫': 3,\n",
       " '5室1厅': 7,\n",
       " '5室2卫': 7,\n",
       " '5室2厅': 49,\n",
       " '5室3卫': 3,\n",
       " '5室3厅': 24,\n",
       " '5室4厅': 1,\n",
       " '5室5厅': 1,\n",
       " '6室0厅': 1,\n",
       " '6室1卫': 1,\n",
       " '6室1厅': 1,\n",
       " '6室2厅': 5,\n",
       " '6室3卫': 2,\n",
       " '6室3厅': 6,\n",
       " '6室4卫': 2,\n",
       " '7室1厅': 1,\n",
       " '7室2厅': 2,\n",
       " '7室3厅': 3,\n",
       " '7室4厅': 1,\n",
       " '8室4厅': 2,\n",
       " '9室1厅': 2,\n",
       " '9室2厅': 1,\n",
       " '9室5厅': 2}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去掉统计数量较少的值\n",
    "house_data = dict((key, value) for key, value in house_info.items() if value > 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_houses = pd.DataFrame({\"户型\": [x for x in house_data.keys()],\n",
    "              \"数量\": [x for x in house_data.values()]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>户型</th>\n",
       "      <th>数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1室0厅</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1室1卫</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1室1厅</td>\n",
       "      <td>844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2室1卫</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2室1厅</td>\n",
       "      <td>2249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2室2厅</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3室1卫</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3室1厅</td>\n",
       "      <td>766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3室2厅</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4室1厅</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4室2厅</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      户型    数量\n",
       "0   1室0厅   244\n",
       "1   1室1卫   126\n",
       "2   1室1厅   844\n",
       "3   2室1卫   120\n",
       "4   2室1厅  2249\n",
       "5   2室2厅   265\n",
       "6   3室1卫    92\n",
       "7   3室1厅   766\n",
       "8   3室2厅   489\n",
       "9   4室1厅    58\n",
       "10  4室2厅   191"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_houses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bgVHA6Oj9DcCSaJujgPlA15Td7cyMmeY4VdLtVlJSwrXXXktxcTHFxcW8+eabFBYWMnfuXJYsWcJ7771XFYcREalVqlVmTHf/0N2PK2d/+5wZc1+UlJTw7LPP0qNHD4YMGUKTJk3YuHEj27dvp6ysjPz8/AM9hIhIrRNLkKlEZswzCS2Zk4CrzeygCnaZyIx5irv3Ah4DbnT3Xu5e6O4by6lHpdMvd+jQgTvuuIP58+ezZs0aDj30UIqKimjfvj3HHnss7du3r8ypi4hIkmqXGbMc+5UZ090fdveT3P2kOgc12WvZgoICzjzzzJ3v33jjDa699lqWL1/O559/zuuvv76vhxcRqfWqVWbMvYg9M+Z9993H0UcfzZVXXsm7777L1q1badeuHQD169dn48a0jSUREdmLjDwnk5QZs4+7nwM8S+juOsfdTwdWWFIGs5RtO5OBzJgjR47k8ccf5+STT2bw4MHce++9PPjgg/Ts2ZOysjL69esX5+FFRGqkapUZE/h3tF2HpHLfJgOZMdu0acPs2bN3+2zu3LlVeQgRkVpHmTFFRCQ2OTElizJjiojkppwIMlVBmTFFRDJPszCLiEhsFGRERCQ2CjIiIhKbWjMmU1H65dpAKaZFJNPUkhERkdgoyIiISGyylX4538zqV9G+qiSfjIiIVL1stWRuAy6ubGEzu8vMlprZvNQXsNDMDomvqjXTfffdx5lnnsmaNWs466yzKCwsZPz48QAsXLiQb37zmxQWFlJUVJTlmopILrM45p00s7qE+ca+RpjWfxxQBHwYFakH1AE2R8udgM7uXmpmk6Ll/wAXuPs2M7sFeM/d/7y/darfpqO3Gfbr/d28RkgM/K9YsYJBgwbRsmVLevTowQknnMCll15K165dmTt3LldffTX/9V//xbnnnkvPnj1ZvDi2rNciUs2Z2SJ3P2l/t4+rJXM+8La7nwq0AQ4F3gB+Qki5PAYYGb2/BZgF7DCzXkBddy8EGhOyYUJIv5yXrmvMzPLKm8FZ0hszZgzjxo0DoGnTpmzcuJGvvvoKADPD3SktLWX79u189NFHlJSUZLO6IpLD4rqFuQj4S9SiaQqsc/crzOxSYAihRfMmcBnwvLsPBTCztYS0zbBnAOwC/MjMtgOtovWfEFpEw4D3UythZtcA1wDUadyySk8wV02ePJmuXbvSuXNnAL73ve/xjW98g1/84heMHj2aRo0aceuttzJq1CjeeustDj30UMrKymjWrFmWay4iuSiupGUbAczsTWCNu39gZm2BbwKnAc2AQkJK5pZm9hd3X+fu/4q2GwzsAGZGu8wHFrj77dH6q4AG7v5QBfV4GHgYQndZlZ5kjpo+fTorV65kxowZLF26lGbNmjFz5kz69OlD3759ufjii2nVqhVFRUU0atSI1q1b06JFi2xXW0RyVCxBxsyaAxuBU4BXzawP8C7wOiHd8vnAanefaGZnEDJfrou2HQSMBga6+7Zol0ewK+DIAZg8eTIAy5cvZ8SIETRr1owGDRpQp04dzIzNmzfz0EMPcdRRR9G7d2+OOeYY8vPzs1xrEclVcXWX/RD4h7s/ZWabgIZAN+B6oIwQNLZGCcgaEMZqMLPDgB8B57j7l0n760ia7jA5cDfffDPXXnstAKeccgpHH300o0ePZsiQIfzhD3/gwQcfzHINRSSXxRVkJgBPmtl1hGyXMwB39yIAMxsJrHf3p6LlOmaWRxhbaQPMiMbyHwOmAo3c/fOY6lorFRQU8PLLLwMwf/78PdYtWrQoG9USkRomrjGZ1UDfxLKZnQ3cGA3ak/T5FUn1+Jm7/xL4ZUqZiwiBJrHcm9DyuT6OuouISNXJVPrlGYTWzP5s+0zKrcvzgJPVshERqf5yYhbmlPTLm9n1EGelKTOmiEjmaYJMERGJjYKMiIjERkFGRERikxNjMlVBmTGrJ2XrFKnZ1JIREZHYKMiIiEhslBlTRERik60xmduAfwFPVKawmd1FSBGQLrFJfTM73d1Lq7B+kkGzZ8/m1ltvBUJCtTvvvJO1a9cydepUmjVrxvPPP09+fj4//OEPmT17NieccAITJ07MbqVFpFJiacmYWV0ze8bM5prZY2bW0cz+bWYvm9nLwOnANYllM/sokULZzOqZ2bSUXZYCY929MM3rRAWY3Na7d2+Ki4spLi6mS5cunHjiiSxZsoR58+YxYMAAVq1axaxZs2jUqBGLFi2ioKCA9evXZ7vaIlIJcbVkEpkxLzKz/2NXZsz7ga9SytYDRhEyYzYkJDM7OqXMzsyYyU//Q8iMSZh8U/lictymTZtYtmwZb775JiUlJZx++um0bt2aUaNG8eijj7Js2TJOPvlkevbsSdOmTbNdXRGphLjGZIqA+1IzYwLtgFuBy4EOwE+Bzu4+1N2/dPcyd+8CrEqzzy5AsZkVm9n7ZrbMzIqBuYRUAHsws2vMbKGZLdy+aUPVn6VUqZdeeol+/fqxbt06WrZsyZw5c1i1ahXFxcWsW7eO448/ntdff52pU6eycuXKbFdXRCqhWmXG3MsulRmzFpg2bRoXXHABH3zwAZ06dQKgXbt2rF69msaNG9OpUyfq1KnD17/+dT7++GOOOOKILNdYRCoS15hM8+jusVOAZlFmzK8ImTH7Aq8Cj7p7AaFl07KCXR4BfBZHXaV6cHdmz55N37596datGwsXLgRg2bJltGvXbudn27dvZ+XKlRx55JFZrrGIVEZc3WU/BC6Kxk9SM2P+D6G77AdmVgT8nNCy2RtlxqzhFixYQOfOnWnQoAE9e/akefPmdO/enU6dOtGjRw+GDBnC8uXL6d69O1deeSVt2rTJdpVFpBIsjvFyMzsceJIQXP5NyHjp7r4jWr9HZsyU9cvcvUP0vinwsruflLT/q6hEd1my+m06epthv66K05MqpGllRKo3M1uU/Pu7r6pVZkzCID6JABM5C2XGFBHJScqMKSIiscmJWZirIjOmiIhkXk4Emaqg9MsiIpmnWZhFRCQ2CjIiIhKbWtNdtq+ZMXVrrYjIgVNLRkREYqMgIyIisVGQERGR2OR8+mUREam+stWSuQ24uLKFo6CU8bqWlJTQu3dvTj31VO644w4Ahg0bRmFhIYMGDWLbtm2ZrpKISE7JlfTLdxMSli0ws3VR4rJiM1sTR/0TJk+ezHHHHcfcuXOZO3cur732Gtu2bWPevHl88cUXzJw5M87Di4jkvJxIv+zuNwCYWQFwt7tfEi2/FVP9E8eltLQUd8fdmTdvHmPGjAFgx44dcR5aRKRG2O+WjJnVMbMLylkdR/rl1OPnA2UVlDmg9MtXXHEF69evZ8iQIdSvX5/DDz+cHj168Nxzz5GXl0f//v33eZ8iIrXJXlsyZvYXIPXX+XagOaFlMpykafgTYki/nFqveoTWzqd7K1cV6ZcfffRRWrZsyUUXXUSrVq144YUXGD9+PNOmTaNu3VrzLKuIyH7Z66+ku38rednMTga6AMPdfYCZpf3hNrPmwEZC+uVXo/TL7xLSL08kdKetdveJZnYGIf1ypYMM0Bp4GvjFPmyzz+bMmcOkSZOYPHkyixcv5phjjuHSSy+lqKiIRo0axXloEZEaYa/dZWbW2cymmtlkM5sKbHL3Z4CtFey3qtMv78bdV7l7V3d/dl+221cDBgxg8+bNnHbaadx22208/fTTrFmzhrPPPptevXrx2GOPxXl4EZGcV25LJrpluKe7X2BmQ4Gl7v53M7sYOMrMRgPldUFNAJ40s+sI6ZdnENIrF0X73iP9spnlJdIvp9Tju8A1hBsG8oG2ZlacVKQ+cJO7z9q3U69YvXr1ePHF3ec7Gzt2bFUfRkSkxio3yLj7DjP7hZn1I4x/bDCzMkLLoxR4GziznG2rMv3y48Aj7r5fYyoiIpI9FY1cL3L3y8xsDPA3d58DYGafuvtfzezGyhzkANMv64lHEZEcVVGQ6WFmfybcerzSzIgCTcNofc60LpQZU0Qk8/Y2JlOH0Ho538x+AHwJjDGzlsDYaH29DNVTRERy0N7GZLab2fnR4l8Jz6RMAo5198XRjQGx3kIsIiK5ba+3MLt7YgqYFe6+MlpuE33WEFgQZ+VERCS3VfTEfz5h3OWPwFlmdgPhjrL/A3oBg4Dr4q5kVdjX9MtxUmpnEaktKhr4v4fwlP42MxsUfbY5+vds4NW4KiYiIrmvogkyfwR8Fr2/GPgNhJkAgO6kmbdMREQkoaIgcxlwWPR6BRgBHEfI73Lh/j4gWZWZMaO73EREpBqqKMjkE7rUjgfaEp7030wY/D96L9tVZF8zY95lZkvNbF7qC1iYSHiWa1KzbN5zzz0UFhYyYMAAtmzZwjPPPEOHDh3o1asXvXr1YsOGfU9XICKSTRWNyfwJ6A8sAU4iJB27ELgaeNnM+rp7aboNzWwS0An4D2HCzCLgw2h1PaBPNCcaUbnO7l6ast0F0RP/pcBYd//z/p1m9VNcXLwzy2bv3r15+OGHWbJkCfPmzWP8+PGsWrWKkpISfv7zn3P55Zdnu7oiIvulopbMtcDH0Ws0IclYnrtvAJ4kdJ/twcx6AXXdvRBoDHyLkH/mJ8CNwBhgZPT+FmAWITNm6naJrGAO5KXrGjOzPDOzSp9xNdG6devdsmxu3bqVkpISTj/9dF577TWOOuooSkpKeOCBBzjxxBN3lhURySUV5ZO5O0oQdpC7Lzez9UCLaPUsQosmnbVENwkQAtm/3P03ZnYpMITQonmTMObzvLsPBTCz1O2SdQF+FE2y2Spa/wlQBxgGvJ9aCTO7hjCDM3Uat9zbqWZcx44dAXZm2dywYQMtW7bkhRdeoGfPnhQXF9OtWzd69epFYWEhRx11FD/4wQ8oKCjIbsVFRPZBhakd3X2rmV0dvf+5mU2L3v8duKGcbf4FYGaDgR3AzMpkxky3XbTLfGCBu98erb8KaODuD1VQ9wPOjBmn5CybkyZNokGDBgC0a9eO1atX06dPH1q0aEGdOnVo27Yt//nPfxRkRCSnVNRdBoC7f5j0/q3EezNrX9420XM1o4GB0bjKV4RnbvoSnq951N0LgFsJmTHL2w7gCHbdSl0jfPLJJ9x7771Mnz6dQw45hG7durFw4UIAli1bRrt27bjhhhsoLi6mrKyMlStX7mz9iIjkioqe+P8jsA34M9Db3UdGXV4fA68Bvycpb0zSdocRnrE5x92/jD5OZMYsIwSNrWZ2CdCAMFZT3nYAHUnTHZbLJk2atDPLJsDw4cNp3rw53bt359hjj6VHjx78+Mc/ZsSIEWzZsoWf/vSnNGu2TwlERUSyzvb2qIuZvQYMJ4ydFAKLgI2Ep/37AS+6+4A0240FvksYMwF4DJiYyHyZLjMmYXD/R2m2mwq87O4nJe3/KirRXZasfpuO3mbYrytbPFaaVkZEcoWZLUr+/d1XFY3JbAUSD2c44ZbmfoRUyjvMLG2EcvdfAr9MquTZwIyKMmOmbheVuYikmQXMrDeh5XN9BXUXEZEsq3DgP0l9wrjK94Dm0Q0AJ1RmwwPMjPlMyq3L84CT3f3z/dmfiIhkzr4EmW2Ehyr/BPRy94Fm9n/xVGt37r496f1mdk3SWWnKjCkiknmVurssqewvgS1AnSgNQM49BCkiIplTUZBpBpwCHAnMBw4GHgIGuPsWYH281RMRkVxWUXfZM4R5xN4jPFl/MVAAtDOzz4GJZmb7OxuziIjUbBVNK3NX8rKZne7uc6L3HYGzcyXAVKfMmCIiFakpjzrstbvMzHqa2RNm9mT0UeKhyVsISctmlruxiIjUehWNyRwB/JHQRQawKfr3EsJ0/VPMrEE8VRMRkVxX0ZiMEx7G3GpmhwJNos8/d/dJZtYMOIR9vKU4cWeau3+1rxVOs686ybc4i4hI9VFuS8bMvgF8J7FImPLliGjusobRgP+v3X3dfhxXmTFFRCohNYNu6nLCfffdx5lnngnAhx9+SGFhIT179uSxxx7LVtWBvXeXfQ4kZlx2d7+FcJdZM+BvhB/3LuVtbGaTokDwgpl1NLN/m9nLZvYycDpwTWLZzD5KBAozq5dIJ5AkkRmzMM3rxPKyc4qI5LLkDLpffPEFd911127LM2eGYfEVK1YwadKkndv99re/ZciQIcydO5dfspRpAAAQ30lEQVQJEyawadOm8g4Ru3K7y9x9pZktTvl4q7v/FsDMjgammtlp7l6SXCg5w6WZzWZXZsz7CVPTJKsHjCJkxmxISGZ2dGp1iDJjpnaNmVleqG5u3OUmIlJZqRl0mzRpsttywpgxYxg3bhz33XcfAO5OaWkpO3bsYPPmzSxdupQTTzwx8ydA5aaVOQ/4WpRl8qgo38s/3P19MzsvNcBE9iszZqSLmS1Ls88alRlTRKQiqRl0r7vuOurWrbtzuX///kyePJmuXbvSuXPnnduNGjWK73znO1x99dU0a9aMsrKybJ1ChUFmHuHHfAGhNfEroD1wmZkdDvzRzB5MTOGfsL+ZMfdSjxqZGVNEpCLJGXTr1q27x/L06dNZuXIlM2bMYOnSpTzwwANcfPHFTJkyhaZNm9K9e3datWqVtfpX9DDmCmBFunVmdjAwODXAJK3fLcOlmSUyY04EzgdWu/tEMzuDkBlzb0HmCPRMjojUMokMukVFRTRq1GiPZYDJkycDsHz5ckaMGMHIkSMZP348K1as4Oabb6a0tJT27ctNYhy7fZmFeTfuvhF4Mt26/c2MuRc1LjOmiEhFUjPoDhw4cI+MusOHD99juyuvvJJBgwZx7rnncv/992OWvbmM95oZc793up+ZMZPWL3P3DtH7ptSwzJgiIhWpLtPKxJ0Zc7/sb2ZMYG60fYekYmehzJgiIjkpliCTSpkxRURqp4wEmQNVFZkxRUQk83IiyFQFpV8WEcm8fUm/LCIisk8UZEREJDa1prvsQDNjVpfbCUVEcolaMiIiEhsFGRERiY2CjIiIxCYrQcbM8s2sfjaOLSIimZOtlsy+pl/Oj5KTZd3WrVsZOHDgzuXUVKibN2/m3HPPpWvXrlx55ZUol5qI1Gax/XBXcfrlu4FiM1tgZuvMrDh6rYmr/umUlZXRrVs3XnrpJWDP1KgzZ87kqaeeom3btrz99tuUlJTsLCsiUhvFcgtzVadfdvcbov0WAHe7+yXR8ltx1L88DRs25J133qFDhzB/Z2pqVIBXX32VIUOGANC3b19mzZpF//79M1lNEZFqI66WTLr0y1cA7YBbgcuBDsBPgc7uPtTdv3T3MnfvAqyq6ABmlk/ITbO3MteY2UIzW7h904YDOJ30OnbsSI8ePXZLhfrZZ5/RpEkTABo3bsznn2seTxGpveKa6r+q0y/vxszqEVo7n1ZQj9jTL6emQm3RogUbNoSAtmHDBlq0aBHHYUVEckKcYzK7pV8mdJO9DvQFXgUedfcCQsum5T7uvjXwNPBElVV4PyRSoU6fPp1DDjkEgH79+jFzZsgU/eqrr9KnT59sVlFEJKviGpOp6vTLu3H3VUDXqqvx/klNjTp8+HAuv/xypk6dSpcuXejatSv9+vXLci1FRLInF9Ivfxe4htASygfaAh8kHa4+cJO7z9pbnQ40/bLmLhOR2qg2pF9+HHjE9cCJiEjOyYX0y9uquDoiIpIhtWaqf2XGFBHJvGoxVYuIiNRMCjIiIhKbWtNddqCZMSX7dIefSO5RS0ZERGKjICMiIrFRkBERkdhkLchUZXbMaMYAERGpZrLZktnX7Jh3mdnSKBHabi9gYSLpmdRcX375Jeeddx6nnnoqN910087PR48ezYgRI3Yre95553HnnXdmuooikiLWIJOc5bIKsmOWAmPdvTDN60R3L43zXCT7nn76aQoLC5k7dy5LlizhvffeY/78+RQVFe1WbsqUKSxZsiRLtRSRZLHdwpwmy2UeB5AdE3Agz8zquHvqHGh5hAk2Nb9ZDda0aVNWrFjB9u3bKSsrIy8vj7Fjx3LnnXfuTK+wfv16xo8fz4033sinn+413ZCIZEBsLZnULJfuvrQKsmN2AYrNrNjM3jezZWZWTJhYs2Nq4bgzY0pmDR48mKKiItq3b8+xxx7Ls88+y9ChQ2nVqtXOMmPHjuX222+nQYMGWaypiCRk9GHMA8yOmQ8scPfbo31dBTRw94fK2yATmTElc8aNG8e1117LiBEjuPTSS/n973/PKaecwvr161m7di1Tpkzh1VdfZenSpXzyySd89dVX9O7dm169emW76iK1Vqaf+E9kx5wInA+sdveJZnYGITvm3oLMEcDM2Gso1VZpaenOFkr9+vWZPn06/fv3Z/bs2Tz11FNceOGFXHjhhQBMnDiRVatWKcCIZFmmg8yBZMfsCLwfb/WkOrvuuuu4/PLLmTBhAkcccYSyjorkgIwFmWhwfqa7F0XLe2THNLO8RHbMlG2bAo3c/fNM1Veqn4KCAubOnbvH571796Z37967fXbVVVdlplIislexB5mkLJdnATfuZ3bMs4CpSdv0JrR8ro+n1iIiUhUy1pI5wOyYz6Q81T8POFktGxGR6i1npvpPfjbG3TcDm/dle2XGFBHJPE2QKSIisVGQERGR2CjIiIhIbHJmTOZAVUX6ZaX/FRHZN2rJiIhIbBRkREQkNjUiM6aIiFRPuZQZMz+amibrtm7dysCBA3cuDxs2jMLCQgYNGsS2bdsAuOeeeygsLGTAgAFs2bIlW1UVEcmqXMqMeTchl8wCM1sX5ZQpNrM1cZ5DqrKyMrp168ZLL70EQHFxMdu2bWPevHl88cUXzJw5kw8++IAlS5Ywb948BgwYwKpV6VLjiIjUfDmTGdPdb4j2WwDc7e6XRMtvxXMG6TVs2JB33nmHDh3C1GqtW7dmzJgxAOzYEeb2fOWVVygpKeH000+ndevWjBo1KpNVFBGpNnItM+ZuzCyfkDagvPWxZ8bs2LEjPXr04LnnniMvL4/+/fuzbt06WrZsyZw5c1i1ahXFxcWxHFtEpLrLpcyYqfuqR2jtlJvIPVOZMV944QXGjx/PtGnTqFu3Lo0bN6ZTp04AtGvXjtWrV8d1aBGRai3TA+mJzJh9gVeBR929gNCyabmP+2oNPA08UZUV3FeffPIJ9957L9OnT+eQQw4BoFu3bixcuBCAZcuW0a5du2xWUUQka3IpM+Zu3H0V0LXKa7iPJk2axJo1azj77LMBGD58OMOHD+eJJ56ge/fuHHvssfTo0SPLtRQRyQ5zj60XKRzAbJm7d0jcfpzIfJkuM2ZYvXP9skTiMjP7LnANoSWUD7QFPkg6TH3gJnefVV496rfp6G2G/fqAzkXTyohIbWNmi9z9pP3dPlcyYz4OPOJxR0QREalSuZIZc1sVV0dERDKg1szCrMyYIiKZVy2maRERkZpJQUZERGKjICMiIrGpNWMyVZEZ80DpFmgRqW3UkhERkdgoyIiISGxqRGbMaLYAERGpZrI5JnMb8C8qOcGlmd0FDAFK0qyub2anu3tpFdZPREQOUKxBJpqOf6q7DzSzjkAR8GG0uh7Qx8yGRsudgM7AVmAKIQXAO8DQaDqZUmCsu/85zjrHbdiwYSxdupRWrVoxdepU6taty3333cdf/vIXXn755Z3lRo8ezaZNm3jkkUeyWFsRkQMTW3dZlOFyEWHOssSx3iDMtnwjMAYYGb2/BZgF7ACuAFa5e1dCzpnE9g7kpesaM7M8M7O4zqWqpEvVvGLFCiZNmrRbufnz51NUVJSlWoqIVJ1qlxmTkGvmpWg3rwJ9knbbBSg2s2Ize9/MlplZMWFSzY6pdchEZsx9kS5V85gxYxg3btzOMlu3bmXs2LHceeedWamjiEhVqo6ZMZsDiYjwBaEbDcIU/wvc/fZoX1cBDdz9ofKOl6nMmJXVsWOIg4lUzZ9++ildu3alc+fOO8vce++9DB06lFatWmWrmiIiVSbTA/+JzJgTgfOB1e4+0czOIGTGXEdIp9wkKt+EXemVjwBmZrS2MUhO1fy9732PlStXMmPGDJYuXcoDDzxAUVEReXl5rF+/nrVr1zJlyhQuvPDCbFdbRGS/VMfMmK8A/YFnCV1n/xt93hF4P6O1rWKJVM1FRUU0atSIyZMnA7B8+XJGjBjByJEjGTlyJACzZ8/mqaeeUoARkZyWsSATZcac6e5F0fIemTGjMk8DF5jZO8DbwCtm1hRo5O6fZ6q+cSgvVbOISE0Ve/rlnQcyO5twJ9n2corUBX7m7nPTbHsR0NHd74qWewO/B65390pNSFYV6ZcPlOYuE5FcU+3TLyccYGbMZ1JuXZ4HnJzrLRsRkZouZ2ZhdvftSe83A5uzWB0REamEnAkyB0rpl0VEMk+zMIuISGwUZEREJDYKMiIiEhsFGRERiY2CjIiIxEZBRkREYqMgIyIisVGQERGR2CjIiIhIbDI2QWa2mVkpsDTb9agmWrArT09tp2uxi67FLroWu3Ry90P2d+NaM60MsPRAZhKtScxsoa5FoGuxi67FLroWu5jZwgPZXt1lIiISGwUZERGJTW0KMg9nuwLViK7FLroWu+ha7KJrscsBXYtaM/AvIiKZV5taMiIikmE1PsiYWQMzm25mb5vZk2Zm2a5TJphZdzNbZWbF0atr6nWoDdfGzOqZ2bTo/R7nW9nPsn0eVSHlWqR+PzrVlmthZpPMbJ6ZvWBmB9fy70TytYjlO1HjgwxwBbDK3bsCzYCzslyfTGkGPOjuvdy9F9CdPa9Djb42ZtYQWMSu80p3vpX9LKeluRa7fT/cfSm14FqYWS+grrsXAo2B4dTe70TqtWhDDN+J2hBk+gIvRe9fBfpksS6Z1AwYYmbzzexZoB97XocafW3cvczduwCroo/SnW9lP8tpaa7Fbt+P6K/R2nAt1gK/id7nAbdTS78T7HktYvlO1IYg0xzYEL3/Ajg0i3XJpGXAbe7eg/AXygXseR1q27VJd76V/aymSf1+nEEtuBbu/i93n29mg4EdwN+opd+JNNfin8TwnagNT/x/CjSJ3jeh9kwVsRx4N+n9iex5HQ5O81lNlu67kO4a1Ibrspzdvx+tqPz1yWlmNggYDQwEHqIWfydSrkU+sDhatZwq+k7UhpbMK0D/6H1fYFYW65JJNwCXmFkecDzwQ/a8DrXt2qQ738p+VtOkfj/epRZcCzM7DPgRcK67l1KLvxNprkUs34naEGSeBg43s3eAzwkXqDZ4ALgaeBN4DniUPa9Dbbs26c63sp/VNLt9P9z9H9SOazGM0BU0w8yKgXrU3u9E6rXYRAzfCT2MKSIisakNLRkREckSBRkREYmNgoyIiMRGQUZERGJTG56TEckqM7sFKAYGEB5ie5jwpPUt7r46KnM4YdqXf6Zs3gH4tru/nbkai1QdBRmRGEVTc7wFHAJsI9wm+gVwAtDGzL4GfBStK09Z3PUUiYuCjEi82gE3AicDnxACShNgPfAt4FLgKuBfwJNAaoulM7DVzMz1vIHkIAUZkXitIDysthb4D9AJ+BowBfgTIQhtBv5IaOF8PWV7I0x98kN2TQMjkjMUZETi1ZwQHPKB6YSJCM8mTNvxBrDa3d82s8uAXwFFhMBzCKFV04cwaeH7Wai7yAFTkBGJ1xDgSuAIoDewyd3PNbNHgPMJswBDuNPzHKCAEJjqAz0JA///m9kqi1QdBRmRGLn7b82sLvANQkD5Ilr1NPD/gKOi5c3A/cC/CTNmNwdeBo4ENmayziJVSUFGJH73AzcRBvi3mdks4DpCArE+hG60bxNuBNjKrpZMB8IEjh8Az2S81iJVQEFGJEZmVgj8hPCcTA+gCzAN+DFhTOZFM1sHfBcoiTarTwg2iRbM981srbvPyWTdRaqCZmEWiZGZ5QPu7luTPjvI3TdF7/PcfUfWKigSMwUZERGJjeYuExGR2CjIiIhIbBRkREQkNgoyIiISGwUZERGJzf8PBU8NzqdmG3cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 图形展示房屋类型\n",
    "\n",
    "house_type = show_houses[\"户型\"]\n",
    "house_type_num = show_houses[\"数量\"]\n",
    "\n",
    "plt.barh(range(11), house_type_num)\n",
    "\n",
    "plt.yticks(range(11), house_type)\n",
    "plt.xlim(0, 2500)\n",
    "\n",
    "plt.title(\"北京市各区域租房数量统计\")\n",
    "plt.xlabel(\"数量\")\n",
    "plt.ylabel(\"房屋类型\")\n",
    "\n",
    "# 给每个条上面添加具体数字\n",
    "for x, y in enumerate(house_type_num):\n",
    "    # print(x, y)\n",
    "    plt.text(y+0.5, x-0.2, \"%s\" %y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 平均租金分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all = pd.DataFrame({\"区域\": file_data[\"区域\"].unique(),\n",
    "              \"房租总金额\": [0]*13,\n",
    "              \"总面积\": [0]*13})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>房租总金额</th>\n",
       "      <th>总面积</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>房山</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>昌平</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>海淀</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>石景山</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>西城</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>通州</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>顺义</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       区域  房租总金额  总面积\n",
       "0      东城      0    0\n",
       "1      丰台      0    0\n",
       "2   亦庄开发区      0    0\n",
       "3      大兴      0    0\n",
       "4      房山      0    0\n",
       "5      昌平      0    0\n",
       "6      朝阳      0    0\n",
       "7      海淀      0    0\n",
       "8     石景山      0    0\n",
       "9      西城      0    0\n",
       "10     通州      0    0\n",
       "11    门头沟      0    0\n",
       "12     顺义      0    0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>小区名称</th>\n",
       "      <th>户型</th>\n",
       "      <th>面积(㎡)</th>\n",
       "      <th>价格(元/月)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>万国城MOMA</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>59.11</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>东城</td>\n",
       "      <td>北官厅胡同2号院</td>\n",
       "      <td>3室0厅</td>\n",
       "      <td>56.92</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>东城</td>\n",
       "      <td>和平里三区</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>40.57</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>东城</td>\n",
       "      <td>菊儿胡同</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>57.09</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>东城</td>\n",
       "      <td>交道口北二条35号院</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>42.67</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   区域        小区名称    户型  面积(㎡)  价格(元/月)\n",
       "0  东城     万国城MOMA  1室0厅  59.11    10000\n",
       "1  东城    北官厅胡同2号院  3室0厅  56.92     6000\n",
       "2  东城       和平里三区  1室1厅  40.57     6900\n",
       "3  东城        菊儿胡同  2室1厅  57.09     8000\n",
       "4  东城  交道口北二条35号院  1室1厅  42.67     5500"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_price = file_data[\"价格(元/月)\"].groupby(file_data[\"区域\"]).sum()\n",
    "sum_area = file_data[\"面积(㎡)\"].groupby(file_data[\"区域\"]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all[\"房租总金额\"] = sum_price.values\n",
    "df_all[\"总面积\"] = sum_area.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>房租总金额</th>\n",
       "      <th>总面积</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>3945550</td>\n",
       "      <td>27353.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>4404893</td>\n",
       "      <td>50922.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>1318400</td>\n",
       "      <td>15995.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>2286950</td>\n",
       "      <td>35884.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>房山</td>\n",
       "      <td>726750</td>\n",
       "      <td>15275.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>昌平</td>\n",
       "      <td>2521515</td>\n",
       "      <td>35972.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>20281396</td>\n",
       "      <td>166921.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>海淀</td>\n",
       "      <td>7279350</td>\n",
       "      <td>57210.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>石景山</td>\n",
       "      <td>1156500</td>\n",
       "      <td>13956.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>西城</td>\n",
       "      <td>5636975</td>\n",
       "      <td>37141.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>通州</td>\n",
       "      <td>2719600</td>\n",
       "      <td>46625.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>1048300</td>\n",
       "      <td>20258.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>顺义</td>\n",
       "      <td>2190900</td>\n",
       "      <td>33668.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       区域     房租总金额        总面积\n",
       "0      东城   3945550   27353.99\n",
       "1      丰台   4404893   50922.79\n",
       "2   亦庄开发区   1318400   15995.53\n",
       "3      大兴   2286950   35884.15\n",
       "4      房山    726750   15275.41\n",
       "5      昌平   2521515   35972.92\n",
       "6      朝阳  20281396  166921.72\n",
       "7      海淀   7279350   57210.39\n",
       "8     石景山   1156500   13956.67\n",
       "9      西城   5636975   37141.64\n",
       "10     通州   2719600   46625.23\n",
       "11    门头沟   1048300   20258.20\n",
       "12     顺义   2190900   33668.97"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算各个区域每平方米的房租\n",
    "df_all[\"每平米租金(元)\"] = round(df_all[\"房租总金额\"] / df_all[\"总面积\"], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>房租总金额</th>\n",
       "      <th>总面积</th>\n",
       "      <th>每平米租金(元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>3945550</td>\n",
       "      <td>27353.99</td>\n",
       "      <td>144.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>4404893</td>\n",
       "      <td>50922.79</td>\n",
       "      <td>86.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>1318400</td>\n",
       "      <td>15995.53</td>\n",
       "      <td>82.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>2286950</td>\n",
       "      <td>35884.15</td>\n",
       "      <td>63.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>房山</td>\n",
       "      <td>726750</td>\n",
       "      <td>15275.41</td>\n",
       "      <td>47.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>昌平</td>\n",
       "      <td>2521515</td>\n",
       "      <td>35972.92</td>\n",
       "      <td>70.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>20281396</td>\n",
       "      <td>166921.72</td>\n",
       "      <td>121.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>海淀</td>\n",
       "      <td>7279350</td>\n",
       "      <td>57210.39</td>\n",
       "      <td>127.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>石景山</td>\n",
       "      <td>1156500</td>\n",
       "      <td>13956.67</td>\n",
       "      <td>82.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>西城</td>\n",
       "      <td>5636975</td>\n",
       "      <td>37141.64</td>\n",
       "      <td>151.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>通州</td>\n",
       "      <td>2719600</td>\n",
       "      <td>46625.23</td>\n",
       "      <td>58.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>1048300</td>\n",
       "      <td>20258.20</td>\n",
       "      <td>51.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>顺义</td>\n",
       "      <td>2190900</td>\n",
       "      <td>33668.97</td>\n",
       "      <td>65.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       区域     房租总金额        总面积  每平米租金(元)\n",
       "0      东城   3945550   27353.99    144.24\n",
       "1      丰台   4404893   50922.79     86.50\n",
       "2   亦庄开发区   1318400   15995.53     82.42\n",
       "3      大兴   2286950   35884.15     63.73\n",
       "4      房山    726750   15275.41     47.58\n",
       "5      昌平   2521515   35972.92     70.09\n",
       "6      朝阳  20281396  166921.72    121.50\n",
       "7      海淀   7279350   57210.39    127.24\n",
       "8     石景山   1156500   13956.67     82.86\n",
       "9      西城   5636975   37141.64    151.77\n",
       "10     通州   2719600   46625.23     58.33\n",
       "11    门头沟   1048300   20258.20     51.75\n",
       "12     顺义   2190900   33668.97     65.07"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merge = pd.merge(new_df, df_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>数量</th>\n",
       "      <th>房租总金额</th>\n",
       "      <th>总面积</th>\n",
       "      <th>每平米租金(元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>282</td>\n",
       "      <td>3945550</td>\n",
       "      <td>27353.99</td>\n",
       "      <td>144.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>577</td>\n",
       "      <td>4404893</td>\n",
       "      <td>50922.79</td>\n",
       "      <td>86.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>147</td>\n",
       "      <td>1318400</td>\n",
       "      <td>15995.53</td>\n",
       "      <td>82.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>362</td>\n",
       "      <td>2286950</td>\n",
       "      <td>35884.15</td>\n",
       "      <td>63.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>房山</td>\n",
       "      <td>180</td>\n",
       "      <td>726750</td>\n",
       "      <td>15275.41</td>\n",
       "      <td>47.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>昌平</td>\n",
       "      <td>347</td>\n",
       "      <td>2521515</td>\n",
       "      <td>35972.92</td>\n",
       "      <td>70.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>1597</td>\n",
       "      <td>20281396</td>\n",
       "      <td>166921.72</td>\n",
       "      <td>121.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>海淀</td>\n",
       "      <td>605</td>\n",
       "      <td>7279350</td>\n",
       "      <td>57210.39</td>\n",
       "      <td>127.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>石景山</td>\n",
       "      <td>175</td>\n",
       "      <td>1156500</td>\n",
       "      <td>13956.67</td>\n",
       "      <td>82.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>西城</td>\n",
       "      <td>442</td>\n",
       "      <td>5636975</td>\n",
       "      <td>37141.64</td>\n",
       "      <td>151.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>通州</td>\n",
       "      <td>477</td>\n",
       "      <td>2719600</td>\n",
       "      <td>46625.23</td>\n",
       "      <td>58.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>285</td>\n",
       "      <td>1048300</td>\n",
       "      <td>20258.20</td>\n",
       "      <td>51.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>顺义</td>\n",
       "      <td>297</td>\n",
       "      <td>2190900</td>\n",
       "      <td>33668.97</td>\n",
       "      <td>65.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       区域    数量     房租总金额        总面积  每平米租金(元)\n",
       "0      东城   282   3945550   27353.99    144.24\n",
       "1      丰台   577   4404893   50922.79     86.50\n",
       "2   亦庄开发区   147   1318400   15995.53     82.42\n",
       "3      大兴   362   2286950   35884.15     63.73\n",
       "4      房山   180    726750   15275.41     47.58\n",
       "5      昌平   347   2521515   35972.92     70.09\n",
       "6      朝阳  1597  20281396  166921.72    121.50\n",
       "7      海淀   605   7279350   57210.39    127.24\n",
       "8     石景山   175   1156500   13956.67     82.86\n",
       "9      西城   442   5636975   37141.64    151.77\n",
       "10     通州   477   2719600   46625.23     58.33\n",
       "11    门头沟   285   1048300   20258.20     51.75\n",
       "12     顺义   297   2190900   33668.97     65.07"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 图形可视化\n",
    "\n",
    "num = df_merge[\"数量\"]\n",
    "price = df_merge[\"每平米租金(元)\"]\n",
    "lx = df_merge[\"区域\"]\n",
    "l = [i for i in range(13)]\n",
    "\n",
    "fig = plt.figure(figsize=(10, 8), dpi=100)\n",
    "\n",
    "# 显示折线图\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.plot(l, price, \"or-\", label=\"价格\")\n",
    "for i, (_x, _y) in enumerate(zip(l, price)):\n",
    "    plt.text(_x+0.2, _y, price[i])\n",
    "ax1.set_ylim([0, 160])   \n",
    "ax1.set_ylabel(\"价格\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "\n",
    "# 显示条形图\n",
    "ax2 = ax1.twinx()\n",
    "plt.bar(l, num, label=\"数量\", alpha=0.2, color=\"green\")\n",
    "ax2.set_ylabel(\"数量\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xticks(l, lx)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 面积基本分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "房屋最大面积是1133平米\n",
      "房屋最小面积是11平米\n",
      "房租最高价格为每月150000元\n",
      "房屋最低价格为每月566元\n"
     ]
    }
   ],
   "source": [
    "# 查看房屋的最大面积和最小面积\n",
    "print('房屋最大面积是%d平米'%(file_data['面积(㎡)'].max()))\n",
    "print('房屋最小面积是%d平米'%(file_data['面积(㎡)'].min()))\n",
    "\n",
    "# 查看房租的最高值和最小值\n",
    "print('房租最高价格为每月%d元'%(file_data['价格(元/月)'].max()))\n",
    "print('房屋最低价格为每月%d元'%(file_data['价格(元/月)'].min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 面积划分\n",
    "area_divide = [1, 30, 50, 70, 90, 120, 140, 160, 1200]\n",
    "area_cut = pd.cut(list(file_data[\"面积(㎡)\"]), area_divide)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "      <th>freqs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categories</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(1, 30]</th>\n",
       "      <td>41</td>\n",
       "      <td>0.007102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(30, 50]</th>\n",
       "      <td>710</td>\n",
       "      <td>0.122986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50, 70]</th>\n",
       "      <td>1566</td>\n",
       "      <td>0.271263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(70, 90]</th>\n",
       "      <td>1094</td>\n",
       "      <td>0.189503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(90, 120]</th>\n",
       "      <td>1082</td>\n",
       "      <td>0.187424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(120, 140]</th>\n",
       "      <td>381</td>\n",
       "      <td>0.065997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(140, 160]</th>\n",
       "      <td>274</td>\n",
       "      <td>0.047462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(160, 1200]</th>\n",
       "      <td>625</td>\n",
       "      <td>0.108263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             counts     freqs\n",
       "categories                   \n",
       "(1, 30]          41  0.007102\n",
       "(30, 50]        710  0.122986\n",
       "(50, 70]       1566  0.271263\n",
       "(70, 90]       1094  0.189503\n",
       "(90, 120]      1082  0.187424\n",
       "(120, 140]      381  0.065997\n",
       "(140, 160]      274  0.047462\n",
       "(160, 1200]     625  0.108263"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_cut_num = area_cut.describe()\n",
    "area_cut_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 图像可视化\n",
    "area_per = (area_cut_num[\"freqs\"].values)*100\n",
    "\n",
    "labels  = ['30平米以下', '30-50平米', '50-70平米', '70-90平米',\n",
    "'90-120平米','120-140平米','140-160平米','160平米以上']\n",
    "\n",
    "plt.figure(figsize=(20, 8), dpi=100)\n",
    "# plt.axes(aspect=1)\n",
    "\n",
    "plt.pie(x=area_per, labels=labels, autopct=\"%.2f %%\")\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
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   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
